IBM Machine Learning for z/OS vs. Pytorch

IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.

Incentivized

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

Incentivized

• Good machine learning tool
• Easy integration

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• Provides Benchmark datasets to test your custom algorithm
• Provides with a lot of pre-coded neural net components to use for your flow
• Gives a framework to write really abstract code.

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• Proper usage of REST API documentation is missing.
• Not localization friendly, cannot support regional or local language documents.

Incentivized

• Distributed data parallel still seems to be complicated
• Support for easy deployment to servers
• Torchvision to have support for latest models with pertained weights

Incentivized

IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.

Incentivized

We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.

Incentivized

As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks. You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.

Incentivized

• Create secure business environment.
• Save upto 90% of manual labor.
• Improve my sales and marketing ROI.

Incentivized

• I'd estimate I can build a model 50% faster on pytorch vs other frameworks

Incentivized